2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Seed-CTS: Unleashing the Power of Tree Search for Superior Performance in Competitive Coding Tasks
Hao Wang, Boyi Liu, Yufeng Zhang +1
Competition-level code generation tasks pose significant challenges for current state-of-the-art large language models (LLMs). For example, on the LiveCodeBench-Hard dataset, model…
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
Shenao Zhang, Zhihan Liu, Boyi Liu +6
Preference alignment in Large Language Models (LLMs) has significantly improved their ability to adhere to human instructions and intentions. However, existing direct alignment alg…
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data
Xuwu Wang, Qiwen Cui, Yunzhe Tao +16
Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exempl…